import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]=32		, _SCREAMING_SNAKE_CASE     :				int=10		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=100		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=1026		, _SCREAMING_SNAKE_CASE     :				List[str]=True		, _SCREAMING_SNAKE_CASE     :				Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"		, _SCREAMING_SNAKE_CASE     :				int="igf_context_pairs.jbl"		, ):
						set_seed(3			)
						# generate train_data and objective_set
						UpperCamelCase_,UpperCamelCase_		:							List[str]  =		generate_datasets(
						    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, number=_SCREAMING_SNAKE_CASE		, min_len=1026		, trim=_SCREAMING_SNAKE_CASE			)
						# keeps model same across runs
						set_seed(4			)
						# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
						# can we train on GPU?
						UpperCamelCase_		:							Any  =		torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""			)

						# load pretrained model
						UpperCamelCase_		:							Union[str, Any]  =		load_gpta("""gpt2"""			).to(_SCREAMING_SNAKE_CASE			)
						print("""computing perplexity on objective set"""			)
						UpperCamelCase_		:							Dict  =		compute_perplexity(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			).item()
						print("""perplexity on objective set:"""		, _SCREAMING_SNAKE_CASE			)

						# collect igf pairs and save to file demo.jbl
						collect_objective_set(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						# clean up, delete model and data we don't need anymore
						del model, train_data, objective_set
						torch.cuda.empty_cache()






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Union[str, Any]		, _SCREAMING_SNAKE_CASE     :				List[Any]=15		, _SCREAMING_SNAKE_CASE     :				Tuple=128		, _SCREAMING_SNAKE_CASE     :				int=100		, _SCREAMING_SNAKE_CASE     :				Tuple="igf_model.pt"		, ):
						set_seed(42			)

						# Load pre-trained model
						UpperCamelCase_		:							Tuple  =		GPTaLMHeadModel.from_pretrained("""gpt2"""			)

						# Initialize secondary learner to use embedding weights of model
						UpperCamelCase_		:							List[str]  =		SecondaryLearner(_SCREAMING_SNAKE_CASE			)

						# Train secondary learner
						UpperCamelCase_		:							Any  =		train_secondary_learner(
						    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, max_epochs=_SCREAMING_SNAKE_CASE		, batch_size=_SCREAMING_SNAKE_CASE		, eval_freq=100		, igf_model_path=_SCREAMING_SNAKE_CASE		, )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]=32		, _SCREAMING_SNAKE_CASE     :				int=1000		, _SCREAMING_SNAKE_CASE     :				List[Any]=16		, _SCREAMING_SNAKE_CASE     :				Optional[Any]=1.0		, _SCREAMING_SNAKE_CASE     :				Optional[int]=recopy_gpta		, _SCREAMING_SNAKE_CASE     :				List[Any]=None		, _SCREAMING_SNAKE_CASE     :				Tuple=10		, _SCREAMING_SNAKE_CASE     :				Tuple="gpt2_finetuned.pt"		, ):
						UpperCamelCase_		:							Union[str, Any]  =		torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""			)
						UpperCamelCase_		:							List[str]  =		RandomSampler(_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_		:							Any  =		DataLoader(_SCREAMING_SNAKE_CASE		, sampler=_SCREAMING_SNAKE_CASE			)

						UpperCamelCase_		:							List[Any]  =		max_steps // (len(_SCREAMING_SNAKE_CASE			)) + 1
						UpperCamelCase_		:							Tuple  =		0
						UpperCamelCase_		:							Tuple  =		torch.zeros((1, context_len)		, dtype=torch.long		, device=_SCREAMING_SNAKE_CASE			)
						UpperCamelCase_,UpperCamelCase_,UpperCamelCase_		:							Optional[int]  =		recopy_model(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)

						model.train()
						if secondary_learner is not None:
												secondary_learner.to(_SCREAMING_SNAKE_CASE			)
												secondary_learner.eval()
						UpperCamelCase_		:							List[str]  =		[]
						UpperCamelCase_		:							int  =		0

						UpperCamelCase_		:							List[str]  =		[]
						UpperCamelCase_		:							Optional[int]  =		[]

						# Compute the performance of the transformer model at the beginning
						UpperCamelCase_		:							int  =		compute_perplexity(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
						test_perps.append(_SCREAMING_SNAKE_CASE			)
						print("""Test perplexity, step"""		, _SCREAMING_SNAKE_CASE		, """:"""		, _SCREAMING_SNAKE_CASE			)
						for epoch in range(int(_SCREAMING_SNAKE_CASE			)			):
												for step, example in enumerate(_SCREAMING_SNAKE_CASE			):
																		torch.cuda.empty_cache()
																		UpperCamelCase_		:							Any  =		random.randint(0		, example.size(2			) - context_len - 1			)
																		UpperCamelCase_		:							str  =		example[0, 0, start : start + context_len]
																		lm_optimizer.zero_grad()
																		UpperCamelCase_		:							Tuple  =		model(_SCREAMING_SNAKE_CASE		, labels=_SCREAMING_SNAKE_CASE			)
																		UpperCamelCase_		:							Any  =		True

																		if secondary_learner is not None:
																								UpperCamelCase_		:							List[Any]  =		secondary_learner.forward(
																								    torch.tensor(_SCREAMING_SNAKE_CASE		, dtype=torch.long		, device=_SCREAMING_SNAKE_CASE			).unsqueeze(0			)			)[0].item()
																								observed_qs.append(float(_SCREAMING_SNAKE_CASE			)			)

																								# Here we implement the simple non-constant threshold for the predicted IG(X) value
																								# We will decay the selectivity of our secondary learner filter from
																								# 1 standard deviation above average to 1 below average after 10 batches.

																								if global_step == 10:
																														UpperCamelCase_		:							List[str]  =		-1
																								if predicted_q < threshold:
																														UpperCamelCase_		:							str  =		False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()			)			)
																								UpperCamelCase_		:							Optional[Any]  =		outputs[0]
																								lm_loss.backward()
																								examples += 1

																		del outputs

																		# Once the batch is filled with enough contexts, backprop on the batch.
																		if examples == batch_size:
																								torch.cuda.empty_cache()
																								UpperCamelCase_		:							int  =		0
																								# Do LM backprop
																								torch.nn.utils.clip_grad_norm_(model.parameters()		, 3.0			)
																								lm_optimizer.step()
																								lm_scheduler.step()  # Update learning rate schedule
																								global_step += 1
																								# Compute the performance of the transformer model at this batch
																								if global_step % eval_interval == 0:
																														UpperCamelCase_		:							str  =		compute_perplexity(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE			)
																														test_perps.append(_SCREAMING_SNAKE_CASE			)

																														print("""Test perplexity, step"""		, _SCREAMING_SNAKE_CASE		, """:"""		, _SCREAMING_SNAKE_CASE			)
            # Break out of the loop after 60 batches
																		if max_steps > 0 and global_step > 60:
																								break
												if max_steps > 0 and global_step > 60:
																		break

    # save finetuned transformer model
						torch.save(model.state_dict()		, _SCREAMING_SNAKE_CASE			)
						torch.cuda.empty_cache()
						# Do some cleaning up so we can reinitialize for the next run of this function
						del lm_optimizer
						del lm_scheduler
						return model






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							Union[str, Any]  =		argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""			)

						# Required parameters
						parser.add_argument(
						    """--data_dir"""		, default=_SCREAMING_SNAKE_CASE		, type=_SCREAMING_SNAKE_CASE		, required=_SCREAMING_SNAKE_CASE		, help="""The input data dir. Should contain data files for WikiText."""		, )
						parser.add_argument(
						    """--model_name_or_path"""		, default=_SCREAMING_SNAKE_CASE		, type=_SCREAMING_SNAKE_CASE		, required=_SCREAMING_SNAKE_CASE		, help="""Path to pretrained model or model identifier from huggingface.co/models"""		, )
						parser.add_argument(
						    """--data_file"""		, type=_SCREAMING_SNAKE_CASE		, default=_SCREAMING_SNAKE_CASE		, help=(
						        """A jbl file containing tokenized data which can be split as objective dataset, """
						        """train_dataset and test_dataset."""
						    )		, )

						parser.add_argument(
						    """--igf_data_file"""		, type=_SCREAMING_SNAKE_CASE		, default=_SCREAMING_SNAKE_CASE		, help="""A jbl file containing the context and information gain pairs to train secondary learner."""		, )

						parser.add_argument(
						    """--output_dir"""		, default=_SCREAMING_SNAKE_CASE		, type=_SCREAMING_SNAKE_CASE		, required=_SCREAMING_SNAKE_CASE		, help="""The output directory where the final fine-tuned model is stored."""		, )

						parser.add_argument(
						    """--tokenizer_name"""		, default=_SCREAMING_SNAKE_CASE		, type=_SCREAMING_SNAKE_CASE		, help="""Pretrained tokenizer name or path if not the same as model_name"""		, )
						parser.add_argument("""--seed"""		, type=_SCREAMING_SNAKE_CASE		, default=_SCREAMING_SNAKE_CASE		, help="""A seed for reproducible training."""			)

						parser.add_argument(
						    """--context_len"""		, default=32		, type=_SCREAMING_SNAKE_CASE		, help=(
						        """The maximum total input sequence length after tokenization. Sequences longer """
						        """than this will be truncated, sequences shorter will be padded."""
						    )		, )

						parser.add_argument(
						    """--size_objective_set"""		, default=100		, type=_SCREAMING_SNAKE_CASE		, help="""number of articles that are long enough to be used as our objective set"""		, )
						parser.add_argument(
						    """--eval_freq"""		, default=100		, type=_SCREAMING_SNAKE_CASE		, help="""secondary model evaluation is triggered at eval_freq"""			)

						parser.add_argument("""--max_steps"""		, default=1000		, type=_SCREAMING_SNAKE_CASE		, help="""To calculate training epochs"""			)

						parser.add_argument(
						    """--secondary_learner_batch_size"""		, default=128		, type=_SCREAMING_SNAKE_CASE		, help="""batch size of training data for secondary learner"""		, )

						parser.add_argument(
						    """--batch_size"""		, default=16		, type=_SCREAMING_SNAKE_CASE		, help="""batch size of training data of language model(gpt2) """			)

						parser.add_argument(
						    """--eval_interval"""		, default=10		, type=_SCREAMING_SNAKE_CASE		, help=(
						        """decay the selectivity of our secondary learner filter from"""
						        """1 standard deviation above average to 1 below average after 10 batches"""
						    )		, )

						parser.add_argument(
						    """--number"""		, default=100		, type=_SCREAMING_SNAKE_CASE		, help="""The number of examples split to be used as objective_set/test_data"""			)

						parser.add_argument(
						    """--min_len"""		, default=1026		, type=_SCREAMING_SNAKE_CASE		, help="""The minimum length of the article to be used as objective set"""			)

						parser.add_argument(
						    """--secondary_learner_max_epochs"""		, default=15		, type=_SCREAMING_SNAKE_CASE		, help="""number of epochs to train secondary learner"""			)

						parser.add_argument("""--trim"""		, default=_SCREAMING_SNAKE_CASE		, type=_SCREAMING_SNAKE_CASE		, help="""truncate the example if it exceeds context length"""			)

						parser.add_argument(
						    """--threshold"""		, default=1.0		, type=_SCREAMING_SNAKE_CASE		, help=(
						        """The threshold value used by secondary learner to filter the train_data and allow only"""
						        """ informative data as input to the model"""
						    )		, )

						parser.add_argument("""--finetuned_model_name"""		, default="""gpt2_finetuned.pt"""		, type=_SCREAMING_SNAKE_CASE		, help="""finetuned_model_name"""			)

						parser.add_argument(
						    """--recopy_model"""		, default=_SCREAMING_SNAKE_CASE		, type=_SCREAMING_SNAKE_CASE		, help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""		, )

						# function calls
						# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
						generate_n_pairs(
						    context_len=32		, max_steps=10		, size_objective_set=100		, min_len=1026		, trim=_SCREAMING_SNAKE_CASE		, data_file="""data/tokenized_stories_train_wikitext103.jbl"""		, igf_data_file="""igf_context_pairs.jbl"""		, )

						# Load train data for secondary learner
						UpperCamelCase_		:							Any  =		joblib.load("""data/IGF_values.jbl"""			)

						# Train secondary learner
						UpperCamelCase_		:							Optional[Any]  =		training_secondary_learner(
						    _SCREAMING_SNAKE_CASE		, secondary_learner_max_epochs=15		, secondary_learner_batch_size=128		, eval_freq=100		, igf_model_path="""igf_model.pt"""		, )

						# load pretrained gpt2 model
						UpperCamelCase_		:							Tuple  =		GPTaLMHeadModel.from_pretrained("""gpt2"""			)
						set_seed(42			)

						# Generate train and test data to train and evaluate gpt2 model
						UpperCamelCase_,UpperCamelCase_		:							Tuple  =		generate_datasets(
						    context_len=32		, file="""data/tokenized_stories_train_wikitext103.jbl"""		, number=100		, min_len=1026		, trim=_SCREAMING_SNAKE_CASE			)

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, context_len=32		, max_steps=1000		, batch_size=16		, threshold=1.0		, recopy_model=_SCREAMING_SNAKE_CASE		, secondary_learner=_SCREAMING_SNAKE_CASE		, eval_interval=10		, finetuned_model_name="""gpt2_finetuned.pt"""		, )


if __name__ == "__main__":
	main()







import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)


def 	lowerCamelCase					(	UpperCamelCase     :				int							)   ->    Union[str, Any]:
		_lowerCamelCase				   =  torch.exp(UpperCamelCase							)
		_lowerCamelCase				   =  torch.sum(UpperCamelCase			,       dim=1							)  # sum of exp(x_i)
		_lowerCamelCase				   =  torch.sum(x * exp_x			,       dim=1							)  # sum of x_i * exp(x_i)
		return torch.log(UpperCamelCase							) - B / A





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Any		)      ->	Optional[Any]:
				super().__init__()
				_lowerCamelCase				   =  config.output_attentions
				_lowerCamelCase				   =  config.output_hidden_states
				_lowerCamelCase				   =  nn.ModuleList([BertLayer(snake_case__		) for _ in range(config.num_hidden_layers		)]		)
				_lowerCamelCase				   =  nn.ModuleList([BertHighway(snake_case__		) for _ in range(config.num_hidden_layers		)]		)

				_lowerCamelCase				   =  [-1 for _ in range(config.num_hidden_layers		)]
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     str		)      ->	Optional[int]:
				if (type(snake_case__		) is float) or (type(snake_case__		) is int):
						for i in range(len(self.early_exit_entropy		)		):
								_lowerCamelCase				   =  x
				else:
						_lowerCamelCase				   =  x
		def   _snake_case       (       self     :     Tuple     ,  snake_case__     :     Optional[int]		)      ->	int:
				_lowerCamelCase				   =  pooler.state_dict()
				for highway in self.highway:
						for name, param in highway.pooler.state_dict().items():
								param.copy_(loaded_model[name]		)



		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]     ,  snake_case__     :     int=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Dict=None     ,  )      ->	List[str]:
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				_lowerCamelCase				   =  ()
				for i, layer_module in enumerate(self.layer		):
						if self.output_hidden_states:
								_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

						_lowerCamelCase				   =  layer_module(
						    snake_case__     ,  snake_case__     ,  head_mask[i]     ,  snake_case__     ,  snake_case__		)
						_lowerCamelCase				   =  layer_outputs[0]

						if self.output_attentions:
								_lowerCamelCase				   =  all_attentions + (layer_outputs[1],)

						_lowerCamelCase				   =  (hidden_states,)
						if self.output_hidden_states:
								_lowerCamelCase				   =  current_outputs + (all_hidden_states,)
						if self.output_attentions:
								_lowerCamelCase				   =  current_outputs + (all_attentions,)

						_lowerCamelCase				   =  self.highway[i](snake_case__		)
						# logits, pooled_output

						if not self.training:
								_lowerCamelCase				   =  highway_exit[0]
								_lowerCamelCase				   =  entropy(snake_case__		)
								_lowerCamelCase				   =  highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

								if highway_entropy < self.early_exit_entropy[i]:
										_lowerCamelCase				   =  (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
										raise HighwayException(snake_case__     ,  i + 1		)
						else:
								_lowerCamelCase				   =  all_highway_exits + (highway_exit,)

        # Add last layer
				if self.output_hidden_states:
						_lowerCamelCase				   =  all_hidden_states + (hidden_states,)

				_lowerCamelCase				   =  (hidden_states,)
				if self.output_hidden_states:
						_lowerCamelCase				   =  outputs + (all_hidden_states,)
				if self.output_attentions:
						_lowerCamelCase				   =  outputs + (all_attentions,)

				_lowerCamelCase				   =  outputs + (all_highway_exits,)
				return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits





@add_start_docstrings(
    'The Bert Model transformer with early exiting (DeeBERT). '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Dict     ,  snake_case__     :     List[Any]		)      ->	Any:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config

				_lowerCamelCase				   =  BertEmbeddings(snake_case__		)
				_lowerCamelCase				   =  DeeBertEncoder(snake_case__		)
				_lowerCamelCase				   =  BertPooler(snake_case__		)

				self.init_weights()
		def   _snake_case       (       self     :     Tuple		)      ->	Tuple:
				self.encoder.init_highway_pooler(self.pooler		)
		def   _snake_case       (       self     :     int		)      ->	List[Any]:
				return self.embeddings.word_embeddings
		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     List[Any]		)      ->	Any:
				_lowerCamelCase				   =  value
		def   _snake_case       (       self     :     List[Any]     ,  snake_case__     :     int		)      ->	Any:
				for layer, heads in heads_to_prune.items():
						self.encoder.layer[layer].attention.prune_heads(snake_case__		)



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     Any=None     ,  snake_case__     :     List[str]=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     List[Any]=None     ,  )      ->	List[str]:
				if input_ids is not None and inputs_embeds is not None:
						raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time'		)
				elif input_ids is not None:
						_lowerCamelCase				   =  input_ids.size()
				elif inputs_embeds is not None:
						_lowerCamelCase				   =  inputs_embeds.size()[:-1]
				else:
						raise ValueError('You have to specify either input_ids or inputs_embeds'		)

				_lowerCamelCase				   =  input_ids.device if input_ids is not None else inputs_embeds.device

				if attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if encoder_attention_mask is None:
						_lowerCamelCase				   =  torch.ones(snake_case__     ,  device=snake_case__		)
				if token_type_ids is None:
						_lowerCamelCase				   =  torch.zeros(snake_case__     ,  dtype=torch.long     ,  device=snake_case__		)

				# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
				# ourselves in which case we just need to make it broadcastable to all heads.
				_lowerCamelCase				   =  self.get_extended_attention_mask(snake_case__     ,  snake_case__     ,  snake_case__		)

				# If a 2D ou 3D attention mask is provided for the cross-attention
				# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
				if encoder_attention_mask.dim() == 3:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, :, :]
				if encoder_attention_mask.dim() == 2:
						_lowerCamelCase				   =  encoder_attention_mask[:, None, None, :]

				_lowerCamelCase				   =  encoder_extended_attention_mask.to(
				    dtype=next(self.parameters()		).dtype		)  # fp16 compatibility
				_lowerCamelCase				   =  (1.0 - encoder_extended_attention_mask) * -1_0000.0

				# Prepare head mask if needed
				# 1.0 in head_mask indicate we keep the head
				# attention_probs has shape bsz x n_heads x N x N
				# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
				# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
				_lowerCamelCase				   =  self.get_head_mask(snake_case__     ,  self.config.num_hidden_layers		)

				_lowerCamelCase				   =  self.embeddings(
				    input_ids=snake_case__     ,  position_ids=snake_case__     ,  token_type_ids=snake_case__     ,  inputs_embeds=snake_case__		)
				_lowerCamelCase				   =  self.encoder(
				    snake_case__     ,  attention_mask=snake_case__     ,  head_mask=snake_case__     ,  encoder_hidden_states=snake_case__     ,  encoder_attention_mask=snake_case__     ,  )
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)

				_lowerCamelCase				   =  (
				    sequence_output,
				    pooled_output,
				) + encoder_outputs[
				    1:
				]  # add hidden_states and attentions if they are here
				return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict     ,  snake_case__     :     Any		)      ->	str:
				_lowerCamelCase				   =  message
				_lowerCamelCase				   =  exit_layer  # start from 1!





class     lowerCAmelCase__     (				nn.Module			):


		'''simple docstring'''






		def __init__(       self     :     Union[str, Any]     ,  snake_case__     :     Optional[Any]		)      ->	Any:
				super().__init__()
				_lowerCamelCase				   =  BertPooler(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  config.num_labels		)



		def   _snake_case       (       self     :     Optional[Any]     ,  snake_case__     :     Dict		)      ->	Optional[Any]:
				# Pooler
				_lowerCamelCase				   =  encoder_outputs[0]
				_lowerCamelCase				   =  self.pooler(snake_case__		)
				# "return" pooler_output

				# BertModel
				_lowerCamelCase				   =  (pooler_input, pooler_output) + encoder_outputs[1:]
				# "return" bmodel_output

				# Dropout and classification
				_lowerCamelCase				   =  bmodel_output[1]

				_lowerCamelCase				   =  self.dropout(snake_case__		)
				_lowerCamelCase				   =  self.classifier(snake_case__		)

				return logits, pooled_output





@add_start_docstrings(
    'Bert Model (with early exiting - DeeBERT) with a classifier on top,\n    also takes care of multi-layer training. '     ,SCREAMING_SNAKE_CASE_     ,)
class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def __init__(       self     :     Optional[int]     ,  snake_case__     :     Dict		)      ->	Dict:
				super().__init__(snake_case__		)
				_lowerCamelCase				   =  config.num_labels
				_lowerCamelCase				   =  config.num_hidden_layers

				_lowerCamelCase				   =  DeeBertModel(snake_case__		)
				_lowerCamelCase				   =  nn.Dropout(config.hidden_dropout_prob		)
				_lowerCamelCase				   =  nn.Linear(config.hidden_size     ,  self.config.num_labels		)

				self.init_weights()



		@add_start_docstrings_to_model_forward(snake_case__		)
		def   _snake_case       (       self     :     Union[str, Any]     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     Optional[int]=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Dict=None     ,  snake_case__     :     str=None     ,  snake_case__     :     Union[str, Any]=None     ,  snake_case__     :     int=None     ,  snake_case__     :     Union[str, Any]=-1     ,  snake_case__     :     Any=False     ,  )      ->	Union[str, Any]:
				_lowerCamelCase				   =  self.num_layers
				try:
						_lowerCamelCase				   =  self.bert(
						    snake_case__     ,  attention_mask=snake_case__     ,  token_type_ids=snake_case__     ,  position_ids=snake_case__     ,  head_mask=snake_case__     ,  inputs_embeds=snake_case__     ,  )
						# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

						_lowerCamelCase				   =  outputs[1]

						_lowerCamelCase				   =  self.dropout(snake_case__		)
						_lowerCamelCase				   =  self.classifier(snake_case__		)
						_lowerCamelCase				   =  (logits,) + outputs[2:]  # add hidden states and attention if they are here
				except HighwayException as e:
						_lowerCamelCase				   =  e.message
						_lowerCamelCase				   =  e.exit_layer
						_lowerCamelCase				   =  outputs[0]

				if not self.training:
						_lowerCamelCase				   =  entropy(snake_case__		)
						_lowerCamelCase				   =  []
						_lowerCamelCase				   =  []
				if labels is not None:
						if self.num_labels == 1:
								#  We are doing regression
								_lowerCamelCase				   =  MSELoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1		)     ,  labels.view(-1		)		)
						else:
								_lowerCamelCase				   =  CrossEntropyLoss()
								_lowerCamelCase				   =  loss_fct(logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)

						# work with highway exits
						_lowerCamelCase				   =  []
						for highway_exit in outputs[-1]:
								_lowerCamelCase				   =  highway_exit[0]
								if not self.training:
										highway_logits_all.append(snake_case__		)
										highway_entropy.append(highway_exit[2]		)
								if self.num_labels == 1:
										#  We are doing regression
										_lowerCamelCase				   =  MSELoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1		)     ,  labels.view(-1		)		)
								else:
										_lowerCamelCase				   =  CrossEntropyLoss()
										_lowerCamelCase				   =  loss_fct(highway_logits.view(-1     ,  self.num_labels		)     ,  labels.view(-1		)		)
								highway_losses.append(snake_case__		)

						if train_highway:
								_lowerCamelCase				   =  (sum(highway_losses[:-1]		),) + outputs
								# exclude the final highway, of course
						else:
								_lowerCamelCase				   =  (loss,) + outputs
				if not self.training:
						_lowerCamelCase				   =  outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
								_lowerCamelCase				   =  (
								    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
								)  # use the highway of the last layer

				return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)